Rapid appraisal of rainfall threshold and selected landslides in Baguio, Philippines

Nolasco-Javier, Dymphna; Kumar, Lalit; Tengonciang, Arlene Mae P.

doi:10.1007/s11069-015-1790-y

Cited by 31 publications

(16 citation statements)

References 27 publications

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“…Among the affected places is the municipality of Tublay, province of Benguet (Figure 1). Predisposing geologic features, together with high rainfall (~ 3892 mm per year) and human activities have placed the area at high risk for landslides and related hazards (Nolasco-Javier et al, 2015). Among the most damaging landslide events were those during Typhoon Parma in October 2009.…”

Section: Introductionmentioning

confidence: 99%

Frequency Ratio Landslide Susceptibility Estimation in a Tropical Mountain Region

Nolasco-Javier

Kumar

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> In a high-rainfall, landslide-prone region in this tropical mountain region, a landslide database was constructed from high resolution satellite imagery (HRSI), local reports and field observations. The landslide data was divided into training (80%) and validation sets (20%). From the digital elevation model (DEM), scanned maps and HRSI, twelve landslide conditioning factors were derived and analysed in a GIS environment: elevation, slope angle, slope aspect, plan curvature, profile curvature, distance to drainage, soil type, lithology, distance to fault/lineament, land use/land cover, distance to road and normalized difference vegetation index (NDVI). Landslide susceptibility was then estimated using the frequency ratio method as applied on the training data. The detailed procedure is explained herein. The landslide model generated was then evaluated using the validation data set. Results demonstrate that the very high, high, moderate, low and very low susceptibility classes included an average of 86%, 7%, 4%, 3% and 1% of the training cells, and 84%, 7%, 5%, 3% and 1% of the validation cells, respectively. Success and prediction rates obtained were 90% and 89%, respectively. The sound output has discriminated well the landslide prone areas and thus may be used in landslide hazard mitigation for local planning.</p>

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Section: Introductionmentioning

confidence: 99%

Frequency Ratio Landslide Susceptibility Estimation in a Tropical Mountain Region

Nolasco-Javier

Kumar

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The slope gradient is one of the main parameters of geomorphology, represent elevation points which influences on the slide susceptibility, since the driving force of the landslides, is the gravity (Torizin, 2011). The slope is typically considered to be one of the influential factors for landslide modeling because it controls the shear forces acting on hill slopes (Bui et al, 2011;Dou et al, 2009;Nolasco-Javier et al, 2015). Slope cuts can change the angle of slope and will reduce the resistance of the landslide.…”

Section: Causative Factorsmentioning

confidence: 99%

Landslide susceptibility analysis in Kabandungan District and Salak Geothermal Field, West Java

Misbahudin

2020

JGLITrop

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Landslide hazards can be caused by several factors such as lithology, land cover, rainfall, slope, curvature, aspect, distance from river and road. In this study, a landslide susceptibility mapping was carried out using a Geographical Information System (GIS) in Kabandungan District and Salak Geothermal Field, West Java. The data used consisted of an inventory of points and landslide areas totalling 247 using a visual collection of Google Earth imagery. The Weight of Evidence (WoE) model is used to select parameters that cause landslides and to produce landslide vulnerability maps. The results of modeling indicate a positive relationship between selective factors for the occurrence of landslides, with Area Under Curve value of 0.89359; 0.76395; 0.75277; 0.73280 and 0.69093 respectively. Landslide susceptibility maps are made by adding up the WoE values for all the most influential parameters. Higher total WoE value is indicating a higher probability of landslide. Landslide susceptibility maps can be used as an effort to prevent potential hazards or mitigate landslides. In addition, this map can also be used furtherly for spatial planning and engineering activities.

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“…The variation in elevation may be related to different environmental settings such as vegetation types and rainfall [ 46 ]. Slope angle is typically considered to be one of the influential factor for landslide modeling because it controls the shear forces acting on hill slopes [ 29 , 47 , 48 ]. Slope aspect, that relates to sunlight exposure and drying winds control the soil moisture were also considered an important factor in landslide studies [ 17 ].…”

Section: Study Area and Spatial Datamentioning

confidence: 99%

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan

et al. 2015

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This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.

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Rapid appraisal of rainfall threshold and selected landslides in Baguio, Philippines

Cited by 31 publications

References 27 publications

Frequency Ratio Landslide Susceptibility Estimation in a Tropical Mountain Region

Frequency Ratio Landslide Susceptibility Estimation in a Tropical Mountain Region

Landslide susceptibility analysis in Kabandungan District and Salak Geothermal Field, West Java

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan

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